
"""simple docstring"""






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
					import numpy as np
					import torch

					from . import (
					    AlbertForPreTraining,
					    BartForConditionalGeneration,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    CamembertForMaskedLM,
					    CTRLLMHeadModel,
					    DistilBertForMaskedLM,
					    DistilBertForQuestionAnswering,
					    DPRContextEncoder,
					    DPRQuestionEncoder,
					    DPRReader,
					    ElectraForPreTraining,
					    FlaubertWithLMHeadModel,
					    GPTaLMHeadModel,
					    LayoutLMForMaskedLM,
					    LxmertForPreTraining,
					    LxmertVisualFeatureEncoder,
					    OpenAIGPTLMHeadModel,
					    RobertaForMaskedLM,
					    RobertaForSequenceClassification,
					    TaForConditionalGeneration,
					    TransfoXLLMHeadModel,
					    XLMRobertaForMaskedLM,
					    XLMWithLMHeadModel,
					    XLNetLMHeadModel,
					)


logging.set_verbosity_info()

UpperCAmelCase        =				{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}




def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		int,					_lowerCamelCase:		Dict,					_lowerCamelCase:		int=False,					_lowerCamelCase:		Optional[int]=True			)					->						List[str]:

	'''simple docstring'''




	if model_type not in MODEL_CLASSES:
		raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

	lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  MODEL_CLASSES[model_type]

	# Initialise TF model
	if config_file in aws_config_map:
		lowerCAmelCase												=  cached_file(_lowerCamelCase,					_lowerCamelCase,					force_download=not use_cached_models			)
	lowerCAmelCase												=  config_class.from_json_file(_lowerCamelCase			)
	lowerCAmelCase												=  True
	lowerCAmelCase												=  True
	print(F"""Building TensorFlow model from configuration: {config}"""			)
	lowerCAmelCase												=  model_class(_lowerCamelCase			)

	# Load weights from tf checkpoint
	if pytorch_checkpoint_path in aws_config_map.keys():
		lowerCAmelCase												=  cached_file(
		    _lowerCamelCase,					_lowerCamelCase,					force_download=not use_cached_models			)
	# Load PyTorch checkpoint in tf2 model:
	lowerCAmelCase												=  load_pytorch_checkpoint_in_tfa_model(_lowerCamelCase,					_lowerCamelCase			)

	if compare_with_pt_model:
		lowerCAmelCase												=  tf_model(tf_model.dummy_inputs,					training=_lowerCamelCase			)  # build the network

		lowerCAmelCase												=  torch.load(_lowerCamelCase,					map_location='''cpu'''			)
		lowerCAmelCase												=  pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=_lowerCamelCase,					config=_lowerCamelCase,					state_dict=_lowerCamelCase			)

		with torch.no_grad():
			lowerCAmelCase												=  pt_model(**pt_model.dummy_inputs			)

		lowerCAmelCase												=  pto[0].numpy()
		lowerCAmelCase												=  tfo[0].numpy()
		lowerCAmelCase												=  np.amax(np.abs(np_pt - np_tf			)			)
		print(F"""Max absolute difference between models outputs {diff}"""			)
		assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""

	# Save pytorch-model
	print(F"""Save TensorFlow model to {tf_dump_path}"""			)
	tf_model.save_weights(_lowerCamelCase,					save_format='''h5'''			)




def __magic_name__	(				_lowerCamelCase:		Dict,					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		int=None,					_lowerCamelCase:		int=None,					_lowerCamelCase:		List[str]=False,					_lowerCamelCase:		Optional[int]=False,					_lowerCamelCase:		Dict=False,					_lowerCamelCase:		Dict=False,					)					->						str:

	'''simple docstring'''




	if args_model_type is None:
		lowerCAmelCase												=  list(MODEL_CLASSES.keys()			)
	else:
		lowerCAmelCase												=  [args_model_type]

	for j, model_type in enumerate(_lowerCamelCase,					start=1			):
		print('''=''' * 100			)
		print(F""" Converting model type {j}/{len(_lowerCamelCase			)}: {model_type}"""			)
		print('''=''' * 100			)
		if model_type not in MODEL_CLASSES:
			raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

		lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  MODEL_CLASSES[model_type]

		if model_shortcut_names_or_path is None:
			lowerCAmelCase												=  list(aws_model_maps.keys()			)
		if config_shortcut_names_or_path is None:
			lowerCAmelCase												=  model_shortcut_names_or_path

		for i, (model_shortcut_name, config_shortcut_name) in enumerate(
		    zip(_lowerCamelCase,					_lowerCamelCase			),					start=1			):
			print('''-''' * 100			)
			if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
				if not only_convert_finetuned_models:
					print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""			)
					continue
				lowerCAmelCase												=  model_shortcut_name
			elif only_convert_finetuned_models:
				print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
				continue
			print(
			    F"""    Converting checkpoint {i}/{len(_lowerCamelCase			)}: {model_shortcut_name} - model_type {model_type}"""			)
			print('''-''' * 100			)

			if config_shortcut_name in aws_config_map:
				lowerCAmelCase												=  cached_file(_lowerCamelCase,					_lowerCamelCase,					force_download=not use_cached_models			)
			else:
				lowerCAmelCase												=  config_shortcut_name

			if model_shortcut_name in aws_model_maps:
				lowerCAmelCase												=  cached_file(_lowerCamelCase,					_lowerCamelCase,					force_download=not use_cached_models			)
			else:
				lowerCAmelCase												=  model_shortcut_name

			if os.path.isfile(_lowerCamelCase			):
				lowerCAmelCase												=  '''converted_model'''

			convert_pt_checkpoint_to_tf(
			    model_type=_lowerCamelCase,					pytorch_checkpoint_path=_lowerCamelCase,					config_file=_lowerCamelCase,					tf_dump_path=os.path.join(_lowerCamelCase,					model_shortcut_name + '''-tf_model.h5'''			),					compare_with_pt_model=_lowerCamelCase,					)
			if remove_cached_files:
				os.remove(_lowerCamelCase			)
				os.remove(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
					)
					parser.add_argument(
					    """--model_type""",
					    default=None,
					    type=str,
					    help=(
					        f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
					        """convert all the models from AWS."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_checkpoint_path""",
					    default=None,
					    type=str,
					    help=(
					        """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
					        """If not given, will download and convert all the checkpoints from AWS."""
					    ),
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    help=(
					        """The config json file corresponding to the pre-trained model. \n"""
					        """This specifies the model architecture. If not given and """
					        """--pytorch_checkpoint_path is not given or is a shortcut name """
					        """use the configuration associated to the shortcut name on the AWS"""
					    ),
					)
					parser.add_argument(
					    """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
					)
					parser.add_argument(
					    """--use_cached_models""",
					    action="""store_true""",
					    help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
					)
					parser.add_argument(
					    """--remove_cached_files""",
					    action="""store_true""",
					    help="""Remove pytorch models after conversion (save memory when converting in batches).""",
					)
					parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
					UpperCAmelCase        =				parser.parse_args()

					# if args.pytorch_checkpoint_path is not None:
					#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
					#                                 args.pytorch_checkpoint_path,
					#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
					#                                 args.tf_dump_path,
					#                                 compare_with_pt_model=args.compare_with_pt_model,
					#                                 use_cached_models=args.use_cached_models)
					# else:
					convert_all_pt_checkpoints_to_tf(
					    args.model_type.lower() if args.model_type is not None else None,
					    args.tf_dump_path,
					    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
					    if args.pytorch_checkpoint_path is not None
					    else None,
					    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
					    compare_with_pt_model=args.compare_with_pt_model,
					    use_cached_models=args.use_cached_models,
					    remove_cached_files=args.remove_cached_files,
					    only_convert_finetuned_models=args.only_convert_finetuned_models,
					)




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	int      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	int      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	Dict:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Tuple      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Optional[Any]      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)


